use anyhow::Result;
use async_trait::async_trait;
use chrono::{DateTime, Utc};
use std::collections::HashMap;
use uuid::Uuid;
use super::types::{
AWSSageMakerService, CloudService, ClusterStatus, CostEstimate, CostOptimizationResult,
CostOptimizationStrategy, DeploymentConfig, DeploymentInfo, DeploymentMetrics,
DeploymentResult, DeploymentStatus, EndpointInfo, EndpointStatus, FunctionInvocationResult,
GPUClusterConfig, GPUClusterResult, ImplementationEffort, OptimizationAction,
OptimizationPhase, PerformanceTier, ScalingResult, ScalingStatus, ServerlessDeploymentResult,
ServerlessFunctionConfig, ServerlessStatus, StorageConfig, StoragePerformanceMetrics,
StorageResult, StorageStatus, StorageType, UpdateResult, UpdateStatus,
};
#[async_trait]
impl CloudService for AWSSageMakerService {
async fn deploy_model(
&self,
__deployment_config: &DeploymentConfig,
) -> Result<DeploymentResult> {
let deployment_id = Uuid::new_v4().to_string();
Ok(DeploymentResult {
deployment_id,
status: DeploymentStatus::Creating,
endpoint_url: None,
estimated_completion: Some(Utc::now() + chrono::Duration::minutes(10)),
cost_estimate: Some(CostEstimate {
setup_cost_usd: 0.0,
hourly_cost_usd: 1.50,
storage_cost_usd_per_gb: 0.10,
data_transfer_cost_usd_per_gb: 0.05,
estimated_monthly_cost_usd: 1080.0,
}),
metadata: HashMap::new(),
})
}
async fn get_endpoint(&self, deployment_id: &str) -> Result<EndpointInfo> {
Ok(EndpointInfo {
deployment_id: deployment_id.to_string(),
endpoint_url: format!(
"https://runtime.sagemaker.{}.amazonaws.com/endpoints/{}/invocations",
self.region, deployment_id
),
status: EndpointStatus::InService,
instance_type: "ml.m5.large".to_string(),
instance_count: 1,
auto_scaling_enabled: true,
creation_time: Utc::now(),
last_modified_time: Utc::now(),
model_data_url: None,
})
}
async fn scale_deployment(
&self,
deployment_id: &str,
target_instances: u32,
) -> Result<ScalingResult> {
Ok(ScalingResult {
deployment_id: deployment_id.to_string(),
previous_instance_count: 1,
target_instance_count: target_instances,
scaling_status: ScalingStatus::InProgress,
estimated_completion: Some(Utc::now() + chrono::Duration::minutes(5)),
})
}
async fn get_metrics(
&self,
deployment_id: &str,
time_range: (DateTime<Utc>, DateTime<Utc>),
) -> Result<DeploymentMetrics> {
Ok(DeploymentMetrics {
deployment_id: deployment_id.to_string(),
time_range,
invocations: 1500,
average_latency_ms: 45.2,
error_rate: 0.02,
throughput_per_second: 25.3,
cpu_utilization: 65.5,
memory_utilization: 78.2,
network_in_mb: 123.4,
network_out_mb: 98.7,
costs: HashMap::from([
("compute".to_string(), 15.75),
("storage".to_string(), 2.30),
("data_transfer".to_string(), 0.85),
]),
})
}
async fn update_deployment(
&self,
deployment_id: &str,
config: &DeploymentConfig,
) -> Result<UpdateResult> {
Ok(UpdateResult {
deployment_id: deployment_id.to_string(),
update_status: UpdateStatus::InProgress,
previous_config: config.clone(),
new_config: config.clone(),
estimated_completion: Some(Utc::now() + chrono::Duration::minutes(8)),
})
}
async fn delete_deployment(&self, deployment_id: &str) -> Result<()> {
println!("Deleting AWS SageMaker deployment: {}", deployment_id);
Ok(())
}
async fn list_deployments(&self) -> Result<Vec<DeploymentInfo>> {
Ok(vec![DeploymentInfo {
deployment_id: "sagemaker-endpoint-1".to_string(),
name: "embedding-model-prod".to_string(),
status: DeploymentStatus::InService,
model_name: "TransE-v1.0".to_string(),
instance_type: "ml.m5.large".to_string(),
instance_count: 2,
creation_time: Utc::now() - chrono::Duration::hours(24),
last_modified_time: Utc::now() - chrono::Duration::hours(2),
}])
}
async fn estimate_costs(
&self,
config: &DeploymentConfig,
_duration_hours: u32,
) -> Result<CostEstimate> {
let hourly_rate = match config.instance_type.as_str() {
"ml.t3.medium" => 0.0464,
"ml.m5.large" => 0.115,
"ml.m5.xlarge" => 0.23,
"ml.c5.2xlarge" => 0.408,
"ml.p3.2xlarge" => 3.825,
_ => 0.115,
};
Ok(CostEstimate {
setup_cost_usd: 0.0,
hourly_cost_usd: hourly_rate * config.initial_instance_count as f64,
storage_cost_usd_per_gb: 0.125,
data_transfer_cost_usd_per_gb: 0.09,
estimated_monthly_cost_usd: hourly_rate
* config.initial_instance_count as f64
* 24.0
* 30.0,
})
}
async fn deploy_serverless_function(
&self,
function_config: &ServerlessFunctionConfig,
) -> Result<ServerlessDeploymentResult> {
let function_arn = format!(
"arn:aws:lambda:{}:123456789012:function:{}",
self.region, function_config.function_name
);
Ok(ServerlessDeploymentResult {
function_arn,
function_name: function_config.function_name.clone(),
status: ServerlessStatus::Pending,
invoke_url: Some(format!(
"https://{}.lambda-url.{}.on.aws/",
function_config.function_name, self.region
)),
version: "1".to_string(),
last_modified: Utc::now(),
})
}
async fn invoke_function(
&self,
_function_name: &str,
payload: &[u8],
) -> Result<FunctionInvocationResult> {
let execution_duration = 150 + (payload.len() / 1000) as u32;
Ok(FunctionInvocationResult {
execution_duration_ms: execution_duration,
billed_duration_ms: ((execution_duration + 99) / 100) * 100,
memory_used_mb: 128,
max_memory_used_mb: 256,
response_payload:
b"{\"statusCode\": 200, \"body\": \"Function executed successfully\"}".to_vec(),
log_result: Some(
"START RequestId: 123\nEND RequestId: 123\nREPORT RequestId: 123\tDuration: 150ms"
.to_string(),
),
status_code: 200,
})
}
async fn create_gpu_cluster(
&self,
cluster_config: &GPUClusterConfig,
) -> Result<GPUClusterResult> {
let cluster_id = format!("eks-gpu-{}", Uuid::new_v4());
let hourly_cost = match cluster_config.gpu_type.as_str() {
"V100" => 3.06 * cluster_config.min_nodes as f64,
"A100" => 4.50 * cluster_config.min_nodes as f64,
"T4" => 1.35 * cluster_config.min_nodes as f64,
_ => 2.00 * cluster_config.min_nodes as f64,
};
Ok(GPUClusterResult {
cluster_id,
cluster_name: cluster_config.cluster_name.clone(),
status: ClusterStatus::Creating,
endpoint: format!(
"https://{}.eks.{}.amazonaws.com",
cluster_config.cluster_name, self.region
),
node_count: cluster_config.min_nodes,
total_gpu_count: cluster_config.min_nodes * cluster_config.gpu_count_per_node,
creation_time: Utc::now(),
estimated_hourly_cost: hourly_cost,
})
}
async fn manage_storage(&self, storage_config: &StorageConfig) -> Result<StorageResult> {
let storage_id = format!("s3-{}", Uuid::new_v4());
let monthly_cost = match storage_config.storage_type {
StorageType::ObjectStorage => storage_config.capacity_gb as f64 * 0.023,
StorageType::BlockStorage => storage_config.capacity_gb as f64 * 0.10,
StorageType::FileStorage => storage_config.capacity_gb as f64 * 0.30,
StorageType::DataLake => storage_config.capacity_gb as f64 * 0.021,
};
let performance_metrics = match storage_config.performance_tier {
PerformanceTier::Standard => StoragePerformanceMetrics {
read_iops: 3000,
write_iops: 3000,
throughput_mbps: 125,
latency_ms: 10.0,
},
PerformanceTier::HighPerformance => StoragePerformanceMetrics {
read_iops: 16000,
write_iops: 16000,
throughput_mbps: 1000,
latency_ms: 1.0,
},
_ => StoragePerformanceMetrics {
read_iops: 100,
write_iops: 100,
throughput_mbps: 12,
latency_ms: 100.0,
},
};
Ok(StorageResult {
storage_id,
endpoint: format!(
"s3://{}-bucket-{}.s3.{}.amazonaws.com",
storage_config.storage_type.clone() as u8,
Uuid::new_v4(),
self.region
),
status: StorageStatus::Creating,
actual_capacity_gb: storage_config.capacity_gb,
monthly_cost_estimate: monthly_cost,
performance_metrics,
})
}
async fn optimize_costs(
&self,
optimization_config: &CostOptimizationStrategy,
) -> Result<CostOptimizationResult> {
let mut actions = Vec::new();
let mut total_savings = 0.0;
if optimization_config.use_spot_instances {
actions.push(OptimizationAction {
action_type: "Spot Instances".to_string(),
description: format!(
"Use spot instances for {}% of workload",
optimization_config.spot_instance_percentage * 100.0
),
estimated_savings_usd: 500.0,
implementation_effort: ImplementationEffort::Medium,
});
total_savings += 500.0;
}
if optimization_config.use_reserved_instances {
actions.push(OptimizationAction {
action_type: "Reserved Instances".to_string(),
description: format!(
"Purchase reserved instances for {}% of workload",
optimization_config.reserved_instance_percentage * 100.0
),
estimated_savings_usd: 800.0,
implementation_effort: ImplementationEffort::Low,
});
total_savings += 800.0;
}
if optimization_config.rightsizing_enabled {
actions.push(OptimizationAction {
action_type: "Rightsizing".to_string(),
description: "Optimize instance sizes based on usage patterns".to_string(),
estimated_savings_usd: 300.0,
implementation_effort: ImplementationEffort::Medium,
});
total_savings += 300.0;
}
let implementation_timeline = vec![
OptimizationPhase {
phase_name: "Quick Wins".to_string(),
duration_days: 7,
actions: vec!["Reserved Instance Purchase".to_string()],
expected_savings_usd: 800.0,
},
OptimizationPhase {
phase_name: "Medium Term".to_string(),
duration_days: 30,
actions: vec![
"Spot Instance Implementation".to_string(),
"Rightsizing".to_string(),
],
expected_savings_usd: 800.0,
},
];
Ok(CostOptimizationResult {
estimated_monthly_savings_usd: total_savings,
optimization_actions_taken: actions,
potential_risks: vec![
"Spot instances may be interrupted".to_string(),
"Reserved instances require upfront commitment".to_string(),
],
implementation_timeline,
})
}
}